Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation

One-dimensional convolutional auto-encoder-based feature learning for fault diagnosis of multivariate processes

(2020) Shumei Chen et al.   JOURNAL OF PROCESS CONTROL

Real time detection of acoustic anomalies in industrial processes using sequential autoencoders

(2020) Barış Bayram et al.   EXPERT SYSTEMS

ROC curves for the optimization of one-class model parameters. A case study: Authenticating extra virgin olive oil from a Catalan protected designation of origin

(2020) Itziar Ruisánchez et al.   TALANTA

Unsupervised learning approach for network intrusion detection system using autoencoders

(2019) Hyunseung Choi et al.   JOURNAL OF SUPERCOMPUTING

Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues

(2019) Arwa Aldweesh et al.   KNOWLEDGE-BASED SYSTEMS

A Novel Statistical Analysis and Autoencoder Driven Intelligent Intrusion Detection Approach

(2019) Cosimo Ieracitano et al.   NEUROCOMPUTING

F-measure curves: A tool to visualize classifier performance under imbalance

(2019) Roghayeh Soleymani et al.   PATTERN RECOGNITION

Enhanced Network Anomaly Detection Based on Deep Neural Networks

(2018) Sheraz Naseer et al.   IEEE Access

Variational data generative model for intrusion detection

(2018) Manuel Lopez-Martin et al.   KNOWLEDGE AND INFORMATION SYSTEMS

Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT

(2017) Manuel Lopez-Martin et al.   SENSORS